Federated Concept-Based Models: Interpretable models with distributed supervision

• URL: http://arxiv.org/abs/2602.04093v1
• Date: Wed, 04 Feb 2026 00:04:50 GMT
• Title: Federated Concept-Based Models: Interpretable models with distributed supervision
• Authors: Dario Fenoglio, Arianna Casanova, Francesco De Santis, Mohan Li, Gabriele Dominici, Johannes Schneider, Martin Gjoreski, Marc Langheinrich, Pietro Barbiero, Giovanni De Felice,
• Abstract summary: Concept-based models (CMs) enhance interpretability in deep learning by grounding predictions in human-understandable concepts.<n>Yet, concept annotations are expensive to obtain and rarely available at scale within a single data source.<n>We propose Federated Concept-based Models (F-CMs), a new methodology for deploying CMs in evolving FL settings.
• Score: 18.11830748487309
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Concept-based models (CMs) enhance interpretability in deep learning by grounding predictions in human-understandable concepts. However, concept annotations are expensive to obtain and rarely available at scale within a single data source. Federated learning (FL) could alleviate this limitation by enabling cross-institutional training that leverages concept annotations distributed across multiple data owners. Yet, FL lacks interpretable modeling paradigms. Integrating CMs with FL is non-trivial: CMs assume a fixed concept space and a predefined model architecture, whereas real-world FL is heterogeneous and non-stationary, with institutions joining over time and bringing new supervision. In this work, we propose Federated Concept-based Models (F-CMs), a new methodology for deploying CMs in evolving FL settings. F-CMs aggregate concept-level information across institutions and efficiently adapt the model architecture in response to changes in the available concept supervision, while preserving institutional privacy. Empirically, F-CMs preserve the accuracy and intervention effectiveness of training settings with full concept supervision, while outperforming non-adaptive federated baselines. Notably, F-CMs enable interpretable inference on concepts not available to a given institution, a key novelty with respect to existing approaches.

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